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Deep Learning2024-01-158 min read

Introduction to Neural Networks

A comprehensive guide to understanding the basics of neural networks and deep learning.

neural networksdeep learningAImachine learning

Introduction to Neural Networks

Neural networks are computing systems inspired by biological neural networks that constitute animal brains.

What is a Neural Network?

A neural network is a series of algorithms that attempts to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.

Key Components

  • Neurons: The basic units of computation
  • Weights: Parameters that determine the strength of connections
  • Activation Functions: Non-linear transformations
  • Layers: Input, hidden, and output layers

Mathematical Foundation

A single neuron computes:

y=f(∑i=1nwixi+b)y = f\left(\sum_{i=1}^{n} w_i x_i + b\right)

Where ff is the activation function, wiw_i are weights, xix_i are inputs, and bb is the bias.

Training

Neural networks learn through backpropagation — computing gradients of the loss function with respect to each weight and updating them via gradient descent.

The loss is minimized iteratively:

wt+1=wt−η∇L(wt)w_{t+1} = w_t - \eta \nabla L(w_t)

This process, combined with large datasets and modern hardware, has led to breakthroughs in computer vision, NLP, and beyond.